Data driven decision making, or in other words, who data analysts really are and why do we need them? - part II

Ever wondered if a process, deemed mature, could still use a makeover? In the first part of our article, we analyzed who exactly a data analyst is and whether we really need them. If you are curious about the answer to this question, click here to be taken to the first part of the article. Let’s get into it!

https://www.linkedin.com/pulse/data-driven-decision-making-other-words-who-analysts-rwwtf

The question is: can you improve a process by chance? Well, yes.

Let me show you an example of a process we improved (even though we were just asked to analyze it at that point in time).

There was a process. The process owner was sure the process was mature. We started analyzing data, as this was a ticketed process, one of the obvious things was to analyze the TTR (time to resolve – so the time from creating to resolving the ticket).

The TTRs came up quite high, so I asked my first WHY, and the process owner explained this is what it always is (and it makes sense with this kind of process, as it is one of the ones you cannot rush). He was satisfied with the outcome and wanted to just move forward…  But “something was off with the data” from my perspective, so I started digging deeper.

The good part was, they were using ServiceNow, a tool I was familiar with, so I started getting more and more into the data, pulling additional reports with additional datapoints... And I found a regularity. As it turned out, most of the TTR time was spent in one state (let’s call it “state 3”, as it was a 3rd step in a linear process flow).

This is when my inner process improvement analyst just had to react. I could not contain myself to just the analysis…

I went back to the process owner and I asked about state 3 and why they thought this was the case, that most of the time was spent in here. He had an idea, and we took a look into process flow coded in ServiceNow. Turns out – there was only one way out of state 3 – ticket closure.

This is when I remembered my call with process user… they had issues with the quality of the outcome. And it was state 3 on which the outcome was delivered. What we could not see was dozens (often) of back and forth emails being sent between the ticket assignee and the requestor, where requestor was rejecting the outcome, because the quality was poor. I talked to process users, from both sides (assignee and requestor) and both were tired with emails.

This is where I proposed modifying the process flow in the tool by adding a back loop, that allowed the requestor to put the ticket back from state 3 (which can be explained as  – “I have done my job, please verify the outcome”) to state 2, which meant “please conduct the analysis” – this was a clear sign for the assignee, that the quality was insufficient. We also added a counter on that very back loop, so we can quantify the quality (how many times was it sent back).

Based on the data and my analysis, process owner made a decision to implement both proposed solutions (back loop and the counter).

The TTR has since gone down, because the process got optimized and tailored to user requirements – no one had to switch between ServiceNow and emails again (and waste time searching for the right email). Plus, we got a new KPI – simple quality check (the counter).

If you read all that, I hope now you have a bit more insight into what we do and who we are. I hope you can appreciate us a bit more and understand why we are being a pain and asking millions of questions and why we bother you even after we have delivered the dashboard.

The section below is for those of you who read this article and thought: “oh, this might be my next career”.

How do I become a data analyst? Who is fit for the role?

First of all – if you thought, that a good(!) data analyst is just about numbers and does not need soft skills, think again.

Our skills (top to bottom, starting from the ones we use the most):

•          Communication (written and spoken) – ability to talk to anyone about anything. Ability to ask the right questions is key. You cannot be shy to ask questions – this is your job. The more answers you get, the more info you have on how to connect data and what the conclusions should be. You need to adjust your communication style depending on whether you talk to “business” or “technical” person.

•          Storytelling (a very popular word nowadays) is key – you need to be able to present what you found out in an easily digestible way, that will grasp everyone’s attention and make them interested at what you are saying. We tend use a lot of analogies to present data to non-technical, process-oriented people. If the language you use will be too technical, you will either lose attention or just not deliver the message.

•          Ability to grasp wide concepts and understand processes flows quickly, combined with the ability to step back and see the bigger picture – you cannot view processes or data in a silo, you need to be able to grasp the organization wide view. You also have to be able to explain these concepts to any audience.

•          Ability and willingness to work in a changing environment and jump from topic to topic. You must be able to switch from one process to another. Often, you work on a few processes at the same time. You need to be able to move from one to another in an instant.

•          Ability to LISTEN and understand what the “client” NEEDS instead of what they WANT (trust me, it is NOT always the same thing 😉)

•          Ability to read the data and work with the data and translate “process” to datapoints. This point includes regular data manipulation and visualization.

•          Ability to see what others cannot see and connect the dots as needed – you have to be able to spot patterns and regularities (and irregularities) of data, that no one else sees. You also have to be able to derive conclusions from data.

•          Curiosity – this is how you get to the top of the class! - willingness to get to the bottom of things, to understand the data how no one else understands it. To dig deeper than anyone, to invest in matching data to process and understanding how it flows together.

If you get there (and have all of the above) – this is where you are a (very) good data analyst – the one with an added bonus of being a process analyst. With that – you will be an asset to any team in any company.

Any downsides to being a data analyst?

Sure. Nothing you can’t overcome with logic and “client" education though. 😉